The development of electric vehicles has significant value for the sustainable utilization of energy resources. However, the unreasonable construction of charging stations causes problems such as low user satisfaction, waste of land resources, unstable power systems, and so on. Reasonable planning of the location and capacity of charging stations is of great significance to users, investors and power grids. This paper synthetically considers three indicators of user satisfaction: charging convenience, charging cost and charging time. Considering the load and charging requirements, the model of electric vehicle charging station location and volume is established, and the model based on artificial immune algorithm is used to optimize the solution. An empirical analysis was conducted based on a typical regional survey. The research results show that increasing the density of charging stations, lowering the charging price and shortening the charging time can effectively improve user satisfaction. The constructed site and capacity selection optimization solving model can scientifically guide charging station resource allocation under the constraints of the optimal user comprehensive satisfaction target, improve the capacity of scientific planning and resource allocation of regional electric vehicle charging stations, and support the large-scale promotion and application of electric vehicles.

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

In order to promote the idling noise quality of a single-cylinder gasoline engine, this paper addresses sound source identification and noise control research. The noise was identified by the application of subjective evaluation, acoustic spectrum and sound intensity analysis. It was found that the noise was caused by the anomalous dynamic performance of the timing system under idling conditions. Furthermore, sound and vibration characteristics of timing system were improved by design methodology research of key components. A multi-body dynamic model was established to characterize dynamic characteristics of the timing system under idling conditions. The key factor of producing noise was that the fluctuation of contact force between the chain and guide and transverse displacement of the chain were much higher than those of the allowable design limit. For the lowest design alternation and manufacturing costs, the work analyzed six timing system improvement schemes obtained by cross combination of tensioner blade line and guide strip radian parameters. After that, the optimal design scheme which could improve dynamic performance parameters of the timing system was derived. The design scheme was conducted with a acoustic test of engine to derive the following results. The noise level of a single-cylinder engine under idling conditions decreased by 3 dB(A). The abnormal noise of the original engine was eliminated under subjective evaluation. The sound quality under other working conditions had no apparent deterioration. Research shows that guide and tensioner blade line optimization design could improve dynamic performance of the timing chain system to eliminate abnormal noise, thereby significantly improving the acoustic characteristic of a single-cylinder engine.

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In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.